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A Better Understanding of Granger Causality Analysis: A Big Data Environment

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  • Xiaojun Song
  • Abderrahim Taamouti

Abstract

This paper aims to provide a better understanding of the causal structure in a multivariate time series by introducing several statistical procedures for testing indirect and spurious causal effects. In practice, detecting these effects is a complicated task, since the auxiliary variables that transmit/induce indirect/spurious causality are very often unknown. The availability of hundreds of economic variables makes this task even more difficult since it is generally infeasible to find the appropriate auxiliary variables among all the available ones. In addition, including hundreds of variables and their lags in a regression equation is technically difficult. The paper proposes several statistical procedures to test for the presence of indirect/spurious causality based on big data analysis. Furthermore, it suggests an identification procedure to find the variables that transmit/induce the indirect/spurious causality. Finally, it provides an empirical application where 135 economic variables were used to study a possible indirect causality from money/credit to income.

Suggested Citation

  • Xiaojun Song & Abderrahim Taamouti, 2019. "A Better Understanding of Granger Causality Analysis: A Big Data Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 911-936, August.
    • Handle: RePEc:bla:obuest:v:81:y:2019:i:4:p:911-936
    DOI: 10.1111/obes.12288
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    Cited by:

    1. Adeyinka Adediran & Bola Babajide & Nataliia Osina, 2023. "Exploring the nexus between price and volume changes in the cryptocurrency market," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 498-512, October.
    2. Dajčman Silvo, 2020. "Economic policy and confidence of economic agents – a causal relationship?," Review of Economic Perspectives, Sciendo, vol. 20(4), pages 471-484, December.
    3. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.
    4. Iwanicz-Drozdowska, Małgorzata & Rogowicz, Karol, 2022. "Does the choice of monetary policy tool matter for systemic risk? The curious case of negative interest rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    5. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    6. Xiaowei Fu & Yanlin Liu & Xi Li, 2020. "Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven," Energies, MDPI, vol. 13(16), pages 1-13, August.

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